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Mood’s median test compares the medians of two or more groups. It is often less powerful than the Mann–Whitney U test, but specifically tests for a difference in medians.

The test can be conducted with the *mood.medtest*
function in the *RVAideMemoire* package or with the *median_test* function
in the *coin* package.

##### Appropriate data

• One-way data with two or more groups

• Dependent variable is ordinal, interval, or ratio

• Independent variable is a factor with levels indicating groups

• Observations between groups are independent. That is, not paired or repeated measures data

##### Hypotheses

• Null hypothesis: The medians of values for each group are equal.

• Alternative hypothesis (two-sided): The medians of values for each group are not equal.

##### Interpretation

Significant results can be reported as “The median value of group A was significantly different from group B.”

### Packages used in this chapter

The packages used in this chapter include:

• RVAideMemoire

• coin

The following commands will install these packages if they are not already installed:

if(!require(RVAideMemoire)){install.packages("RVAideMemoire")}

if(!require(coin)){install.packages("coin")}

### Example using the *RVAideMemoire* package

This example uses the formula notation indicating that *Likert*
is the dependent variable and *Speaker* is the independent variable. The *data=*
option indicates the data frame that contains the variables. For the meaning
of other options, see *?mood.medtest*.

For appropriate plots and data frame checking, see the *Two-sample
Mann–Whitney U Test* chapter.

Input =("

Speaker Likert

Pooh 3

Pooh 5

Pooh 4

Pooh 4

Pooh 4

Pooh 4

Pooh 4

Pooh 4

Pooh 5

Pooh 5

Piglet 2

Piglet 4

Piglet 2

Piglet 2

Piglet 1

Piglet 2

Piglet 3

Piglet 2

Piglet 2

Piglet 3

")

Data = read.table(textConnection(Input),header=TRUE)

### Check the data frame

library(psych)

headTail(Data)

str(Data)

summary(Data)

### Remove unnecessary objects

rm(Input)

### Mood’s Median Test

library(RVAideMemoire)

mood.medtest(Likert ~ Speaker,

data = Data,

exact = FALSE)

Mood's median test

X-squared = 9.8, df = 1, p-value = 0.001745

### Median test by Monte Carlo
simulation

library(coin)

median_test(Likert ~ Speaker,

data = Data,

distribution = approximate(B = 10000))

Approximative Two-Sample Brown-Mood Median Test

Z = -3.4871, p-value = 0.0011